CN105349643B - The method and microRNAs markers that serotonin changes after prediction sleep deprivation - Google Patents

Abstract

The present invention provides under sleep deprivation 5 hydroxytryptamines (5 HT) correlation microRNAs markers combine：4701 3p and hsa miR of hsa miR, 4800 5p, and predict the method that 5 hydroxytryptamines (5 HT) change under sleep deprivation and the method that prediction sleep deprivation rings the shadow using it, and provide and detect its kit.

Description

Methods and microRNAs markers to predict changes in serotonin after sleep deprivation

technical field

The invention belongs to the field of physiology, and more specifically the invention relates to physiological changes after sleep deprivation.

Background technique

Research on the effects of sleep on cognitive function has shown that two consecutive nights of sleep restriction of less than 6 hours can lead to reduced cognitive function, including prolonged reaction times, increased errors in simple reaction tasks, reduced mental arithmetic ability, and impaired working memory . Because biological rhythms widely regulate various physiological functions, hormone secretion, behavior, cognitive functions, etc., human physiological functions and working ability cannot avoid being regulated by the internal biological rhythms of the human body to achieve rebalancing, and the imbalance of biological rhythms will seriously affect Human health and performance.

In the modeling research of sleep rhythm disorder, Professor Van Dongen of the United States further pointed out on the basis of the classic "two-process model (two-process model)" that individual differences in sensitivity to sleep loss are important factors that cannot be ignored in modeling , and put forward an individualized prediction model of rhythm disorder and work ability.

Changes in 5-hydroxytryptamine (5-HT) are important physiological effects of sleep deprivation. The correlation between platelets and sleep deprivation has long been reported, and its molecular mechanism is related to 5-hydroxytryptamine (Heiser et al., 1997; Schreiber et al., 1997). It has been documented that sleep deprivation stimulates the release of serotonin (Grossman et al., 2000). Serotonin is closely related to the body's response to sleep deprivation. A recently published study on metabolomics of sleep deprivation showed that 27 metabolites including 5-HT, tryptophan, and taurine were significantly increased after sleep deprivation (Davies et al. 2014).

In summary, it is important to screen epigenetic indicators that can characterize and predict the physiological effects of sleep deprivation, especially the individual differences in 5-hydroxytryptamine (5-HT) changes.

Contents of the invention

The inventors used a human experimental model of sleep deprivation to screen and identify epigenetic indicators that can characterize the parameters of an individualized model of sleep rhythm.

In one aspect, the present invention provides 5-hydroxytryptamine (5-HT) related microRNAs markers hsa-miR-4701-3p and hsa-miR-4800-5p under sleep deprivation, and their sequences are respectively SEQ ID NO.1 and/or SEQ ID NO.2. Preferably, the microRNAs markers can also be selected from the following sequences and/or their complementary sequences: SEQ ID NO.1+SEQ ID NO.2, SEQ ID NO.2+SEQ ID NO.1, SEQ ID NO.1 +SEQ ID NO.2+SEQ ID NO.1 and SEQ ID NO.2+SEQ ID NO.1+SEQ ID NO.2.

SEQ ID NO.1 and SEQ ID NO.2 can characterize serotonin (5-HT) under sleep deprivation. The relationship between 5-hydroxytryptamine (5-HT), hsa-miR-4701-3p and hsa-miR-4800-5p satisfies Y=ax1+bx2+c, Y is the change of 5-hydroxytryptamine (5-HT), x1 and x2 is the miRNA variable hsa-miR-4701-3p and hsa-miR-4800-5p respectively, a and b are the regression coefficients of the variables, and c is a constant, preferably a:b:c=(-1):(-12 to -8): (20 to 30); for example, -0.1, -1 and 2.4, respectively.

In another aspect, the present invention provides a method for predicting changes in 5-hydroxytryptamine (5-HT) under sleep deprivation, said method comprising detecting microRNAs markers hsa-miR-4701-3p and hsa-miR-4800-5p Expression levels, the sequences of hsa-miR-4701-3p and hsa-miR-4800-5p are SEQ ID NO.1 and SEQ ID NO.2, respectively. High expression levels of hsa-miR-4701-3p and hsa-miR-4800-5p indicated a decrease in serotonin (5-HT) levels after sleep deprivation. In a specific embodiment, the serotonin (5-HT) change Y=ax1+bx2+c, where x1 and x2 are the miRNA variables hsa-miR-4701-3p and hsa-miR-4800-5p, a and b, respectively is the regression coefficient of the variable, and c is a constant, preferably a:b:c=(-1):(-12 to -8):(20 to 30); for example -0.1, -1, and 2.4 respectively.

In yet another aspect, the present invention provides a method for predicting the impact of sleep deprivation on humans, said method comprising detecting the expression levels of microRNAs markers hsa-miR-4701-3p and hsa-miR-4800-5p, hsa-miR The sequences of -4701-3p and hsa-miR-4800-5p are SEQ ID NO.1 and SEQ ID NO.2, respectively. High expression levels of hsa-miR-4701-3p and hsa-miR-4800-5p indicated a decrease in serotonin (5-HT) levels after sleep deprivation.

In yet another aspect, the present invention also provides a test kit for detecting the expression levels of microRNAs markers hsa-miR-4701-3p and hsa-miR-4800-5p, said kit comprising a sequence selected from the following and/or its Complementary sequences: hsa-miR-4701-3p and hsa-miR-4800-5p; SEQ ID NO.1+SEQ ID NO.2; SEQ ID NO.2+SEQ ID NO.1; SEQ ID NO.1+SEQ ID NO.1 ID NO.2+SEQ ID NO.1; SEQ ID NO.2+SEQ ID NO.1+SEQ ID NO.2.

Through the mining of serum microRNAs and biochemical indicators, the present invention proves that by detecting the expression level of serum microRNAs, the prediction of individual differences in the physiological effects of sleep deprivation can be realized, thereby screening individuals who are more tolerant to sleep deprivation.

The microRNAs screened in the present invention have a predictive function for changes in the physiological effect of sleep deprivation, 5-hydroxytryptamine (5-HT), and have a high correlation with the physiological regulation of sleep deprivation. This discovery is a systematic reveal of the molecular mechanism of sleep deprivation affecting the human body Provides entirely new clues that are worth digging into.

Description of drawings

Fig. 1 Standard curve for detection of human blood 5-hydroxytryptamine (5-HT) content.

Detailed ways

The present invention provides 5-hydroxytryptamine (5-HT) related microRNAs marker hsa-miR-4701-3p and/or hsa-miR-4800-5p under sleep deprivation, and using it to predict 5-hydroxytryptamine (5-HT) under sleep deprivation -HT) changes and methods for predicting the effects of sleep deprivation on humans.

Although the present invention is not bound by any theory, the inventors believe that there is a negative regulatory relationship between the expression levels of hsa-miR-4701-3p and hsa-miR-4800-5p and 5-hydroxytryptamine (5-HT).

In the present invention, the inventors found that 5-hydroxytryptamine (5-HT) changes can be expressed as Y=ax1+bx2+c, where x1 and x2 are the miRNA variables hsa-miR-4701-3p and hsa-miR-4800- 5p, a and b are the regression coefficients of the variables, and c is a constant. Moreover, the inventors further found that a, b and c preferably have such a proportional relationship a:b:c=(-1):(-12 to -8):(20 to 30). For example, a, b, and c are -0.1, -1, and 2.4, respectively.

Example

1. The physiological effect model of sleep deprivation

(1) Volunteer

A total of 12 volunteers, male, aged 20 to 50, were screened for physical health and mental health. The above 12 volunteers were randomly divided into 4 groups with 3 people in each group. One week before the experiment, each volunteer was required to maintain a regular schedule (sleep from 23:00 to 7:00 the next day).

(2) Experimental conditions

Sleep deprivation lasted 72 hours. The subjects had no sleep for 72 hours from the time they entered the isolation room to the end of the experiment. When the subjects showed drowsiness and drowsiness, they were awakened by the ringing of the bell.

(3) Experimental procedure

On the third day before the experiment, the emotional state and basic physiological and biochemical indicators of all volunteers were tested as the baseline values of the above indicators of the volunteers. The emotional state of the volunteers was tested using the Anxiety and Depression Scale and the POMS questionnaire. A dynamic physiological parameter recorder was used to test the basic physiological indicators of the volunteers, and the saliva and urine were collected at 8:00 in the morning, 12:00 at noon, 16:00 in the afternoon and 20:00 in the evening to analyze the biochemical indicators of the volunteers. Test for 5-hydroxytryptamine (5-HT), using general methods and kits of enzyme-linked immunosorbent assay (ELISA).

On the third day before the experiment, blood was collected at 8:00 in the morning of the day, serum (1ml) was separated, and RNA degradation protection agent was added to freeze for subsequent microRNAs chip analysis.

On the day after the volunteers had a full rest after the experiment, the emotional state and basic physiological and biochemical indicators of the volunteers were tested as the results of the volunteer experiment. The test method and index are the same as the test before the experiment.

2. Overall situation of data collection

The data collection is shown in the table below:

Table 1 Data collection table

3. Determination method and quality control of 5-hydroxytryptamine (5-HT) content

5-HT was tested according to the manual of human 5-hydroxytryptamine (5-HT) ELISA kit (E01H0106, Shanghai Lanji Biotech).

In order to ensure the accuracy of the data, a double standard curve was made on each microplate, and the sample addition operation was performed in the middle of the sample addition process to reduce the error caused by the sample addition time. The results of the standard curve are shown in Figure 1.

The curve fitting adopts Curve Expert professional data analysis software, and the MMF model with the highest fitting degree is selected to analyze the data. The MMF model curve fitting analysis results in the detection of human blood 5-hydroxytryptamine (5-HT) content are as follows:

MMF model: y=(a*b+c*x^d)/(b+x^d)

Coefficient data:

a=4.82E-02

b=6.60E+04

c=2.03E+00

d=1.68E+00

MMF model: y=(a*b+c*x^d)/(b+x^d)

Standard deviation: 0.0731429

Correlation coefficient: 0.9955208

Notes:

More than 100 repetition counts;

The fit failed to converge to a tolerance of 0.000001 (0.010700 for CHI2);

No weighting was used.

For the subjects, the 5-HT indicators were collected at two times "before/after" sleep deprivation, and the subjects were classified according to the index "rise/fall".

The change trend of the subjects' 5-HT index is shown in Table 2.

Table 2 Change trend of 5-HT index

4. microRNAs test

Human microRNAs chips from Agilent were used.

In view of the relative maturity of gene chip technology and the existence of a mature commercial technology service platform in China, the microRNAs chip test is completed by means of outsourced testing. The main technical links include:

(1) Total RNA extraction. Total RNA was extracted using a commercial kit mirVanaTM RNA Isolation Kit (AppliedBiosystem p/n AM1556).

(2) RNA quality inspection. Use the Bio Analyzer RNA6000Nano kit quality inspection to calculate the quality inspection parameters such as RIN=RNAIntegrity Number.

(3) Sample labeling and hybridization, using Agilent miRNA Complete Labeling and Hyb Kit.

(4) Chip scanning, using an Agilent chip scanner, controlled by Agilent Scan Control software.

(5) Data preprocessing and analysis, using GeneSpring GX software.

The above experimental operations were carried out according to the relevant kits and the company's standard operating procedures.

5. MicroRNAs chip data preprocessing

In the standard Agilent chip data output file, the raw value (raw) of 0.1 is an undetected signal (Not Detected). For all 12 samples, use the TotalProbeSignal.raw value to take log2 and then normalize. The method is quantile normalization, and the tool is the analysis package normalize.quantiles{preprocessCore} in R. The miR probes with no signal in 12 subjects were filtered out, leaving 373 probes as input for subsequent analysis.

6. Screening of microRNAs markers for sleep deprivation effect prediction

(1) Classification algorithm

Based on the data characteristics of this project, the multiple regression model based on the Lasso method was selected for classification. The algorithm constrains the regression coefficients of all variables through the penalty discriminant function, and compresses some meaningless or minimally meaningful independent variable coefficients to 0, so that the output function L(F) reaches the maximum value, and the most meaningful model is screened out. variable. Perform 100 random repeated sampling training on the training samples to obtain 100 sets of optimal feature combinations, sort all the features according to the frequency of occurrence, and extract the features with a frequency of more than 50% as the optimal feature set. Use 4-fold cross-validation to evaluate the model accuracy of each training, and record the optimal feature set screened out by each training model, and calculate the AUC area of the model. The ratio of the training set and the test set is selected as 3:1, that is, 9 samples are randomly selected from 12 samples each time for training, and 3 samples are used for testing to evaluate the performance of classification prediction.

(2) Screening of 5-HT stress indicator microRNAs markers

Using the change of 5-HT index as the classification standard, two groups of people with large stress response (sensitive) and small response (insensitive) were distinguished, and microRNAs markers targeting 5-HT stress index were screened through classification algorithms. Through the classification algorithm, the microRNAs markers most related to 5-HT, hsa-miR-4701-3p and hsa-miR-4800-5p, were screened out. From the screening results, it can be seen that there is a negative regulatory relationship between the expression levels of hsa-miR-4701-3p and hsa-miR-4800-5p and 5-HT, that is, hsa-miR-4701-3p and hsa-miR-4701-3p before sleep deprivation The high expression of hsa-miR-4800-5p may lead to the decrease of 5-HT level after sleep deprivation. The screening results are shown in Table 2. The prediction model coefficients are shown in Table 3. The calculation formula used is as follows:

Y(5-ht)=-0.1*hsa-miR-4701-3p-1*hsa-miR-4800-5p+2.4. According to the above results, considering the deviation between different individuals, the inventor provides the following a, b and c proportional relationship a:b:c=(-1):(-12 to-8):(20 to 30) .

Table 2 The collection of microRNAs with predictive ability for biochemical indicators

BioChem microRNAs AUC n_5HT hsa-miR-4701-3p; hsa-miR-4800-5p 100%

Table 3 Prediction model coefficients

(3) Functional enrichment analysis of 5-HT index-related microRNAs target genes

Functional enrichment analysis was carried out for the microRNAs markers of 5-HT stress indicators screened by the classification algorithm. The analysis results in Table 4 show the distribution of microRNAs target genes in various tissues, and the most enriched tissues are the brain and so on. The results indicate that these 5-HT-related microRNAs markers are mainly expressed in the brain, so these microRNAs markers can be used to predict brain-related conditions, such as physiological states.

Table 4 Distribution of microRNAs target genes in tissues predicted by changes in biochemical index 5-HT

organize count % P value brain 164 49.54683 2.96E-04 colon 31 9.365559 0.013665 duodenum 8 2.416918 0.015992 small intestine 4 1.208459 0.030403 fetal kidney 8 2.416918 0.034825

(1)-(3) The results indicated that the high expression of hsa-miR-4701-3p and hsa-miR-4800-5p before sleep deprivation led to the decrease of 5-HT after sleep deprivation, and hsa-miR-4701-3p and hsa-miR-4701-3p hsa-miR-4800-5p is mainly expressed in the brain, so changes in 5-HT-related conditions (such as physiological status) in the brain can be predicted by changes in hsa-miR-4701-3p and hsa-miR-4800-5p, It acts as an early warning for the occurrence of this situation. The inventors also tested SEQ ID NO.1+SEQ ID NO.2, SEQ ID NO.2+SEQ ID NO.1, SEQ ID NO.1+SEQ ID NO.2+SEQ ID NO.1 and SEQ ID NO. 2+SEQ ID NO.1+SEQ ID NO.2 and its complementary sequence were used to detect the effects of hsa-miR-4701-3p and hsa-miR-4800-5p in tissues by hybridization, and found that they can be used to detect hsa- Qualitative changes of miR-4701-3p and hsa-miR-4800-5p, among which the effect of SEQ ID NO.1+SEQ ID NO.2, SEQ ID NO.2+SEQ ID NO.1 is the most ideal. Therefore, if there is no special requirement for quantification, they can all be used to qualitatively predict changes in 5-hydroxytryptamine (5-HT) under sleep deprivation, thereby predicting changes in 5-HT-related conditions (such as physiological states) in the brain.

Claims (6)

1. A method for predicting changes in 5-hydroxytryptamine (5-HT) under sleep deprivation for non-diagnostic purposes, the method comprising detecting the expression levels of microRNAs markers hsa-miR-4701-3p and hsa-miR-4800-5p , the sequences of hsa-miR-4701-3p and hsa-miR-4800-5p are SEQ ID NO.1 and SEQ ID NO.2, respectively, high expression levels of hsa-miR-4701-3p and hsa-miR-4800- 5p indicates a decrease in serotonin (5-HT) levels after sleep deprivation. 2. The method according to claim 1, 5-hydroxytryptamine (5-HT) change Y=ax1+bx2+c, x1 and x2 are variable miRNA markers hsa-miR-4701-3p and hsa-miR-4800- 5p, a and b are regression coefficients of variables, c is a constant, a:b:c =(-1):(-12 to -8):(20 to 30). 3. The method according to claim 2, a and b are respectively -0.1 and -1, and c is 2.4. 4. A method for predicting the impact of sleep deprivation on people for non-diagnostic purposes, said method comprising detecting the levels of expression of microRNAs markers hsa-miR-4701-3p and hsa-miR-4800-5p, hsa-miR-4701- The sequences of 3p and hsa-miR-4800-5p are SEQ ID NO.1 and SEQ ID NO.2, respectively, high expression levels of hsa-miR-4701-3p and hsa-miR-4800-5p indicate that they are more resistant to sleep deprivation by. 5. The use of microRNAs markers hsa-miR-4701-3p and hsa-miR-4800-5p for the preparation of kits for predicting changes in 5-hydroxytryptamine (5-HT) under sleep deprivation or the impact of sleep deprivation on humans, said The kit includes hsa-miR-4701-3p and hsa-miR-4800-5p, wherein the sequences of hsa-miR-4701-3p and hsa-miR-4800-5p are SEQ ID NO.1 and SEQ ID NO. 2. 6. The use according to claim 5, said kit further comprising DNA and/or RNA detection reagents.